Evaluation of Control Interfaces for Desktop Virtual Environments

2015 ◽  
Vol 24 (4) ◽  
pp. 322-334 ◽  
Author(s):  
Tyler Thrash ◽  
Mubbasir Kapadia ◽  
Mehdi Moussaid ◽  
Christophe Wilhelm ◽  
Dirk Helbing ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Precise Measurement ◽  
Movement Patterns ◽  
Human Movement ◽  
Transportation Systems ◽  
Spatial Behavior ◽  
Limited Resources ◽  
Movement Trajectories ◽  
Crowd Management

Tracking and analyzing the movement trajectories of individuals and groups is an important problem with applications in crowd management and the development of transportation systems. However, real-world tracking is limited due to the size of the trackable area and the precision with which a person can be tracked. Experiments in virtual environments have many advantages, including practically unlimited sizes and the precise measurement of spatial behavior. However, the generalizability of research using virtual environments to real-world scenarios is often limited by the translation of participants’ movements to those of their avatars. We compared human movement patterns in virtual environments with different control interfaces: a handheld joystick, a mouse-and-keyboard setup, and a keyboard-only setup. With each of these controls, participants completed several movement-related tasks of varying difficulty in a limited amount of time. Questionnaires indicated that participants preferred the mouse-and-keyboard setup over the other two setups. Standard performance measures suggested that the joystick underperformed in a variety of tasks. Movement trajectories in the final task indicated that each of the control setups produced somewhat realistic behavior, despite some apparent differences from real-world trajectories. Overall, the results indicated that, given limited resources, mouse-and-keyboard setups consistently outperform joysticks and produce realistic movement patterns.

Influence of Physical Characteristics of Routes on Distance Cognition in Virtual Environments

2005 ◽  
Vol 32 (5) ◽  
pp. 777-785 ◽  
Author(s):  
Ebru Cubukcu ◽  
Jack L Nasar
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Virtual Environment ◽  
Real World ◽  
Three Dimensional ◽  
Spatial Behavior ◽  
Self Report ◽  
Parking Garage ◽  
The Real ◽  
Predicting Behavior

Discrepanices between perceived and actual distance may affect people's spatial behavior. In a previous study Nasar, using self report of behavior, found that segmentation (measured through the number of buildings) along the route affected choice of parking garage and path from the parking garage to a destination. We recreated that same environment in a three-dimensional virtual environment and conducted a test to see whether the same factors emerged under these more controlled conditions and to see whether spatial behavior in the virtual environment accurately reflected behavior in the real environment. The results confirmed similar patterns of response in the virtual and real environments. This supports the use of virtual reality as a tool for predicting behavior in the real world and confirms increases in segmentation as related to increases in perceived distance.

Real-World Physical Length Comparison in Virtual Environments

2007 ◽  
Vol 13 (3) ◽  
pp. 19-24
Author(s):  
Chul Hee Jung ◽  
Min-Geun Lee ◽  
Chang Hyuck Im ◽  
이명원
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Physical Length

Recent Advances in Reinforcement Learning for Traffic Signal Control

2021 ◽  
Vol 22 (2) ◽  
pp. 12-18 ◽  
Author(s):  
Hua Wei ◽  
Guanjie Zheng ◽  
Vikash Gayah ◽  
Zhenhui Li
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Methods ◽  
Advantages And Disadvantages ◽  
Recent Advances

Traffic signal control is an important and challenging real-world problem that has recently received a large amount of interest from both transportation and computer science communities. In this survey, we focus on investigating the recent advances in using reinforcement learning (RL) techniques to solve the traffic signal control problem. We classify the known approaches based on the RL techniques they use and provide a review of existing models with analysis on their advantages and disadvantages. Moreover, we give an overview of the simulation environments and experimental settings that have been developed to evaluate the traffic signal control methods. Finally, we explore future directions in the area of RLbased traffic signal control methods. We hope this survey could provide insights to researchers dealing with real-world applications in intelligent transportation systems

scholarly journals Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko
Keyword(s):  
Spatial Scales ◽  
Predictive Ability ◽  
Movement Patterns ◽  
Human Movement ◽  
Continental Scale ◽  
Movement Characteristics ◽  
Human Movements ◽  
Movement Models ◽  
Epidemiology Data

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

Predictive Value of Assessing Vehicle Interior Design Ergonomics in a Virtual Environment

2004 ◽  
Vol 4 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Thomas Reuding ◽  
Pamela Meil
Keyword(s):  
Virtual Environments ◽  
Interior Design ◽  
Real World ◽  
Predictive Value ◽  
Work Processes ◽  
Complex Activity ◽  
Vehicle Interior ◽  
Design Work ◽  
The Real ◽  
The Stability

The predictive value and the reliability of evaluations made in immersive projection environments are limited when compared to the real world. As in other applications of numerical simulations, the acceptance of such techniques does not only depend on the stability of the methods, but also on the quality and credibility of the results obtained. In this paper, we investigate the predictive value of virtual reality and virtual environments when used for engineering assessment tasks. We examine the ergonomics evaluation of a vehicle interior, which is a complex activity relying heavily on know-how gained from personal experience, and compare performance in a VE with performance in the real world. If one assumes that within complex engineering processes certain types of work will be performed by more or less the same personnel, one can infer that a fairly consistent base of experience-based knowledge exists. Under such premises and if evaluations are conducted as comparisons within the VE, we believe that the reliability of the assessments is suitable for conceptual design work. Despite a number of unanswered questions at this time we believe this study leads to a better understanding of what determines the reliability of results obtained in virtual environments, thus making it useful for optimizing virtual prototyping processes and better utilization of the potential of VR and VEs in company work processes.

Big Data Processing and Mining for Next Generation Intelligent Transportation Systems

2013 ◽  
Vol 63 (3) ◽  
Author(s):  
Jelena Fiosina ◽  
Maxims Fiosins, Jörg P. Müller
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real World ◽  
Intelligent Transportation Systems ◽  
Social Impact ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Optimization Techniques ◽  
Next Generation ◽  
Big Data Processing

The deployment of future Internet and communication technologies (ICT) provide intelligent transportation systems (ITS) with huge volumes of real-time data (Big Data) that need to be managed, communicated, interpreted, aggregated and analysed. These technologies considerably enhance the effectiveness and user friendliness of ITS, providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and novel features of ITS in the context of the Internet of Things (IoT) and cloud technologies. In this study, we contend that future service- and cloud-based ITS can largely benefit from sophisticated data processing capabilities. Therefore, new Big Data processing and mining (BDPM) as well as optimization techniques need to be developed and applied to support decision-making capabilities. This study presents real-world scenarios of ITS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies. Decentralised cooperative BDPM methods are reviewed and their effectiveness is evaluated using real-world data models of the city of Hannover, Germany. We point out and discuss future work directions and opportunities in the area of the development of BDPM methods in ITS.

scholarly journals Object detection and tracking in video sequences: formalization, metrics and results

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 43-60
Author(s):  
R. P. Bohush ◽  
S. V. Ablameyko
Keyword(s):  
Moving Objects ◽  
Human Movement ◽  
Video Sequences ◽  
Multiple Objects ◽  
Movement Trajectories ◽  
Detection And Tracking ◽  
Form Features ◽  
One And Many ◽  
Tracking Objects

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

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